HCP_puberty_ph
Visualizations in wb_view
library(plyr)
library(psych)
library(ggplot2)
library(Hmisc)
library(stats)
library(lm.beta)
library(lmtest)
library(ggpubr)
library(dotwhisker)
library(boot)
library(knitr)
library(kableExtra)
library(ggdag)
library(mosaic)
library(mets)
library(xtable)
library(stargazer)
library(yhat)
library(tidyverse)
library(broom)
library(lmSupport)
#devtools::install_github("malcolmbarrett/ggdag")
#devtools::install_github("kkholst/lava")
#devtools::install_github("kkholst/mets")
cbPalette <- c("#CC79A7", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00")
bwPalette<-c("#000000", "#999999", "CCCCCC","666666","333333")Loading datasets and combining
## Mode FALSE TRUE
## logical 510 117## [1] 1003 225The missing check is OK. Lines up with imaging data.
summary(d4$Age_in_Yrs)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22.00 27.00 30.00 29.52 32.00 36.00sd(d4$Age_in_Yrs)## [1] 3.611612summary(d4$Menstrual_AgeBegan)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.00 12.00 13.00 12.72 13.00 18.00sd(d4$Menstrual_AgeBegan)## [1] 1.580688d4$Race <- relevel(d4$Race, "White")
dem1<-lm(Menstrual_AgeBegan ~ Race, data=d4)
summary(dem1)##
## Call:
## lm(formula = Menstrual_AgeBegan ~ Race, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8218 -0.8218 0.1782 0.6667 5.1782
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 12.82181 0.08086 158.566
## RaceAm. Indian/Alaskan Nat. 3.17819 1.57003 2.024
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. -0.48848 0.28467 -1.716
## RaceBlack or African Am. -0.47565 0.19508 -2.438
## RaceMore than one 0.09486 0.45979 0.206
## RaceUnknown or Not Reported -0.52181 0.50238 -1.039
## Pr(>|t|)
## (Intercept) <2e-16 ***
## RaceAm. Indian/Alaskan Nat. 0.0435 *
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.0868 .
## RaceBlack or African Am. 0.0151 *
## RaceMore than one 0.8366
## RaceUnknown or Not Reported 0.2995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.568 on 504 degrees of freedom
## Multiple R-squared: 0.02572, Adjusted R-squared: 0.01605
## F-statistic: 2.661 on 5 and 504 DF, p-value: 0.02183lm.beta(dem1)##
## Call:
## lm(formula = Menstrual_AgeBegan ~ Race, data = d4)
##
## Standardized Coefficients::
## (Intercept)
## 0.000000000
## RaceAm. Indian/Alaskan Nat.
## 0.089032549
## RaceAsian/Nat. Hawaiian/Othr Pacific Is.
## -0.076097171
## RaceBlack or African Am.
## -0.108415353
## RaceMore than one
## 0.009105207
## RaceUnknown or Not Reported
## -0.045814823dem2<-lm(BMI ~ Race, data=d4)
summary(dem2)##
## Call:
## lm(formula = BMI ~ Race, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.2779 -3.9839 -0.8598 2.3667 19.0983
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 25.6017 0.2772 92.351
## RaceAm. Indian/Alaskan Nat. 3.6283 5.3827 0.674
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. -3.9808 0.9760 -4.079
## RaceBlack or African Am. 3.5162 0.6688 5.257
## RaceMore than one -0.4475 1.5764 -0.284
## RaceUnknown or Not Reported 2.9273 1.7224 1.700
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## RaceAm. Indian/Alaskan Nat. 0.5006
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 5.26e-05 ***
## RaceBlack or African Am. 2.16e-07 ***
## RaceMore than one 0.7766
## RaceUnknown or Not Reported 0.0898 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.376 on 504 degrees of freedom
## Multiple R-squared: 0.09516, Adjusted R-squared: 0.08618
## F-statistic: 10.6 on 5 and 504 DF, p-value: 1.071e-09lm.beta(dem2)##
## Call:
## lm(formula = BMI ~ Race, data = d4)
##
## Standardized Coefficients::
## (Intercept)
## 0.00000000
## RaceAm. Indian/Alaskan Nat.
## 0.02857100
## RaceAsian/Nat. Hawaiian/Othr Pacific Is.
## -0.17432086
## RaceBlack or African Am.
## 0.22528491
## RaceMore than one
## -0.01207526
## RaceUnknown or Not Reported
## 0.07224628dem3<-lm(Age_in_Yrs ~ Race, data=d4)
summary(dem3)##
## Call:
## lm(formula = Age_in_Yrs ~ Race, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8723 -2.8723 0.1277 3.1277 9.0303
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 29.8723 0.1808 165.248
## RaceAm. Indian/Alaskan Nat. 5.1277 3.5100 1.461
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. -2.9026 0.6364 -4.561
## RaceBlack or African Am. -0.3339 0.4361 -0.766
## RaceMore than one -3.7057 1.0279 -3.605
## RaceUnknown or Not Reported -1.7723 1.1231 -1.578
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## RaceAm. Indian/Alaskan Nat. 0.144672
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 6.4e-06 ***
## RaceBlack or African Am. 0.444302
## RaceMore than one 0.000343 ***
## RaceUnknown or Not Reported 0.115183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.505 on 504 degrees of freedom
## Multiple R-squared: 0.06725, Adjusted R-squared: 0.05799
## F-statistic: 7.267 on 5 and 504 DF, p-value: 1.379e-06lm.beta(dem3)##
## Call:
## lm(formula = Age_in_Yrs ~ Race, data = d4)
##
## Standardized Coefficients::
## (Intercept)
## 0.00000000
## RaceAm. Indian/Alaskan Nat.
## 0.06286849
## RaceAsian/Nat. Hawaiian/Othr Pacific Is.
## -0.19790868
## RaceBlack or African Am.
## -0.03330682
## RaceMore than one
## -0.15567808
## RaceUnknown or Not Reported
## -0.06810628Nice function to make a pretty table
tablr<-function(Y,x,D){
Q<-favstats(Y ~ x, data = D)
Q.stat <- Q[, c("x", "n", "mean", "sd")]
colnames(Q.stat)<-c("test","n", "mean", "sd")
a<-match.call()[2]
return(Q.stat)
}Making a nice table
demo_vars<-c("Race","BMI","HbA1C","Menstrual_AgeBegan","Age_in_Yrs","group")
dems<-d4[demo_vars]
#dems<-na.omit(dems)
byRace<-apply(dems, MARGIN = 2, FUN = tablr, x=dems$Race, D=dems)
byGroup<-apply(dems, MARGIN = 2, FUN = tablr, x=dems$group, D=dems)
t1<-merge(byRace$BMI,byRace$HbA1C,by="test")
t2<-merge(t1,byRace$Menstrual_AgeBegan,by="test")
t3<-merge(t2, byRace$Age_in_Yrs, by="test")
s1<-merge(byGroup$BMI,byGroup$HbA1C,by="test")
s2<-merge(s1,byGroup$Menstrual_AgeBegan,by="test")
s3<-merge(s2, byGroup$Age_in_Yrs, by="test")
all<-rbind(t3,s3)
row.names(all)<-all$test
row.names(all)<-c("Native American", "Asian, Native Hawaiian, or Pacific Islander","Black or African American","More than one race","Unknown or chose not to report","White","Total")
drops <- c("test")
all<-all[ , !(names(all) %in% drops)]Make the nice table
kable(all, format = "html", col.names = c("n","mean","SD",
"n","mean","SD",
"n","mean","SD",
"n","mean","SD"),
caption = "Table 1: Descriptive statistics by reported race",
digits = c(0, 2, 2, 0, 2, 2,0, 2, 2,0, 2, 2), align = "ccrr") %>%
kable_styling(full_width = FALSE, position = "left") %>%
add_header_above(c(" " = 1,"BMI"= 3,"HbA1C"= 3,"Age of menses onset"=3,"Age at scan"=3))| n | mean | SD | n | mean | SD | n | mean | SD | n | mean | SD | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Native American | 1 | 29.23 | NA | 1 | 5.90 | NA | 1 | 16.00 | NA | 1 | 35.00 | NA |
| Asian, Native Hawaiian, or Pacific Islander | 33 | 21.62 | 3.62 | 21 | 5.23 | 0.31 | 33 | 12.33 | 1.49 | 33 | 26.97 | 4.29 |
| Black or African American | 78 | 29.12 | 6.42 | 44 | 5.40 | 0.35 | 78 | 12.35 | 1.84 | 78 | 29.54 | 3.43 |
| More than one race | 12 | 25.15 | 4.13 | 8 | 5.39 | 0.26 | 12 | 12.92 | 1.51 | 12 | 26.17 | 3.49 |
| Unknown or chose not to report | 10 | 28.53 | 4.73 | 8 | 5.29 | 0.40 | 10 | 12.30 | 1.83 | 10 | 28.10 | 4.23 |
| White | 376 | 25.60 | 5.31 | 245 | 5.21 | 0.37 | 376 | 12.82 | 1.51 | 376 | 29.87 | 3.43 |
| Total | 510 | 25.94 | 5.62 | 327 | 5.25 | 0.37 | 510 | 12.72 | 1.58 | 510 | 29.52 | 3.61 |
Summary of main findings
Earlier onset of puberty significantly related to heavier adult BMI.
int_graph<-ggplot(d4, aes(Menstrual_AgeBegan, BMI)) +
geom_point(shape=1) +
geom_smooth(method=lm,colour='black')+theme_classic()+scale_y_continuous(name="Body Mass Index (BMI)")+
scale_x_continuous(name="Age of onset of menstration (years)")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
int_graph
m1<-lm( BMI ~ Menstrual_AgeBegan, data=d4)
summary(m1)Call: lm(formula = BMI ~ Menstrual_AgeBegan, data = d4)
Residuals: Min 1Q Median 3Q Max -9.342 -4.033 -1.228 2.652 21.408
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.3323 1.9952 16.706 < 2e-16 Menstrual_AgeBegan -0.5817 0.1557 -3.736 0.000209 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ‘’ 1
Residual standard error: 5.553 on 508 degrees of freedom Multiple R-squared: 0.02673, Adjusted R-squared: 0.02482 F-statistic: 13.95 on 1 and 508 DF, p-value: 0.0002086
m2<-lm( BMI ~ Menstrual_AgeBegan+Age_in_Yrs, data=d4)
lrtest(m1,m2)Likelihood ratio test
Model 1: BMI ~ Menstrual_AgeBegan Model 2: BMI ~ Menstrual_AgeBegan + Age_in_Yrs #Df LogLik Df Chisq Pr(>Chisq)
1 3 -1597.0
2 4 -1593.7 1 6.5532 0.01047 * — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ‘’ 1
summary(m2)Call: lm(formula = BMI ~ Menstrual_AgeBegan + Age_in_Yrs, data = d4)
Residuals: Min 1Q Median 3Q Max -9.960 -3.900 -1.099 2.680 20.616
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.44674 2.75285 10.334 < 2e-16 Menstrual_AgeBegan -0.60089 0.15505 -3.875 0.00012 Age_in_Yrs 0.17377 0.06786 2.561 0.01074 *
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ‘’ 1
Residual standard error: 5.523 on 507 degrees of freedom Multiple R-squared: 0.03916, Adjusted R-squared: 0.03537 F-statistic: 10.33 on 2 and 507 DF, p-value: 4e-05
m3<-lm( BMI ~ Menstrual_AgeBegan+Age_in_Yrs+Race, data=d4)
lrtest(m2,m3)Likelihood ratio test
Model 1: BMI ~ Menstrual_AgeBegan + Age_in_Yrs Model 2: BMI ~ Menstrual_AgeBegan + Age_in_Yrs + Race #Df LogLik Df Chisq Pr(>Chisq)
1 4 -1593.7
2 9 -1569.9 5 47.673 4.141e-09 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ‘’ 1
summary(m3)Call: lm(formula = BMI ~ Menstrual_AgeBegan + Age_in_Yrs + Race, data = d4)
Residuals: Min 1Q Median 3Q Max -9.6518 -3.7428 -0.9398 2.2026 19.1630
Coefficients: Estimate Std. Error t value (Intercept) 29.06601 2.76132 10.526 Menstrual_AgeBegan -0.56271 0.15054 -3.738 Age_in_Yrs 0.12556 0.06734 1.865 RaceAm. Indian/Alaskan Nat. 4.77289 5.33583 0.894 RaceAsian/Nat. Hawaiian/Othr Pacific Is. -3.89122 0.98369 -3.956 RaceBlack or African Am. 3.29051 0.66323 4.961 RaceMore than one 0.07111 1.57335 0.045 RaceUnknown or Not Reported 2.85620 1.70302 1.677 Pr(>|t|)
(Intercept) < 2e-16 Menstrual_AgeBegan 0.000207 Age_in_Yrs 0.062825 .
RaceAm. Indian/Alaskan Nat. 0.371484
RaceAsian/Nat. Hawaiian/Othr Pacific Is. 8.73e-05 RaceBlack or African Am. 9.60e-07 RaceMore than one 0.963966
RaceUnknown or Not Reported 0.094138 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ‘’ 1
Residual standard error: 5.297 on 502 degrees of freedom Multiple R-squared: 0.1249, Adjusted R-squared: 0.1127 F-statistic: 10.24 on 7 and 502 DF, p-value: 5.16e-12
lm.beta(m3)Call: lm(formula = BMI ~ Menstrual_AgeBegan + Age_in_Yrs + Race, data = d4)
Standardized Coefficients:: (Intercept) 0.000000000 Menstrual_AgeBegan -0.158175583 Age_in_Yrs 0.080639616 RaceAm. Indian/Alaskan Nat. 0.037584083 RaceAsian/Nat. Hawaiian/Othr Pacific Is. -0.170398293 RaceBlack or African Am. 0.210822093 RaceMore than one 0.001918786 RaceUnknown or Not Reported 0.070491562
effect.size(m3) Effect.Size RecommendedWherry1 0.1109 No Claudy3 0.1131 No Smith 0.111 No Wherry2 0.1127 Yes Olkin & Pratt 0.1113 No Pratt 0.1113 No
modelEffectSizes(m3)lm(formula = BMI ~ Menstrual_AgeBegan + Age_in_Yrs + Race, data = d4)
Coefficients SSR df pEta-sqr dR-sqr (Intercept) 3108.7777 1 0.1808 NA Menstrual_AgeBegan 392.0226 1 0.0271 0.0244 Age_in_Yrs 97.5466 1 0.0069 0.0061 Race 1380.1302 5 0.0892 0.0857
Sum of squared errors (SSE): 14085.0 Sum of squared total (SST): 16095.4
confint(m3) 2.5 % 97.5 %(Intercept) 23.640834256 34.4911811 Menstrual_AgeBegan -0.858480753 -0.2669420 Age_in_Yrs -0.006742434 0.2578558 RaceAm. Indian/Alaskan Nat. -5.710422957 15.2562039 RaceAsian/Nat. Hawaiian/Othr Pacific Is. -5.823875848 -1.9585587 RaceBlack or African Am. 1.987456931 4.5935651 RaceMore than one -3.020056287 3.1622851 RaceUnknown or Not Reported -0.489726041 6.2021250
x<-as.data.frame(summary(m3)[4])
row.names(x)<-c("Intercept","Menstrual age began (yrs)","Age (yrs)", "Asian American, Native Hawaiian, Other Pacific Islander", "Black or African American", "More than one race","Unknown race or not reported", "White")
x## coefficients.Estimate
## Intercept 29.06600768
## Menstrual age began (yrs) -0.56271138
## Age (yrs) 0.12555668
## Asian American, Native Hawaiian, Other Pacific Islander 4.77289046
## Black or African American -3.89121729
## More than one race 3.29051102
## Unknown race or not reported 0.07111442
## White 2.85619947
## coefficients.Std..Error
## Intercept 2.76132290
## Menstrual age began (yrs) 0.15054168
## Age (yrs) 0.06733805
## Asian American, Native Hawaiian, Other Pacific Islander 5.33583189
## Black or African American 0.98369101
## More than one race 0.66323283
## Unknown race or not reported 1.57335439
## White 1.70302035
## coefficients.t.value
## Intercept 10.52611693
## Menstrual age began (yrs) -3.73791081
## Age (yrs) 1.86457269
## Asian American, Native Hawaiian, Other Pacific Islander 0.89449791
## Black or African American -3.95573127
## More than one race 4.96132112
## Unknown race or not reported 0.04519924
## White 1.67713761
## coefficients.Pr...t..
## Intercept 1.509110e-23
## Menstrual age began (yrs) 2.068971e-04
## Age (yrs) 6.282468e-02
## Asian American, Native Hawaiian, Other Pacific Islander 3.714842e-01
## Black or African American 8.728419e-05
## More than one race 9.601408e-07
## Unknown race or not reported 9.639665e-01
## White 9.413808e-02kable(x, format = "html",
caption = "Table 2: Relationship between BMI and Age of onset of menses",
digits = c(2,2,2,2), align = "cccc", col.names = c("Estimate", "Standard Error","T value","P value")) %>%
kable_styling()| Estimate | Standard Error | T value | P value | |
|---|---|---|---|---|
| Intercept | 29.07 | 2.76 | 10.53 | 0.00 |
| Menstrual age began (yrs) | -0.56 | 0.15 | -3.74 | 0.00 |
| Age (yrs) | 0.13 | 0.07 | 1.86 | 0.06 |
| Asian American, Native Hawaiian, Other Pacific Islander | 4.77 | 5.34 | 0.89 | 0.37 |
| Black or African American | -3.89 | 0.98 | -3.96 | 0.00 |
| More than one race | 3.29 | 0.66 | 4.96 | 0.00 |
| Unknown race or not reported | 0.07 | 1.57 | 0.05 | 0.96 |
| White | 2.86 | 1.70 | 1.68 | 0.09 |
Summary
Age of menstration is significantly related to adult BMI. This relationship remains significant with the addition of age and race in the model. Both age and race improve the model fit.
Imaging data
img<-read.table("~/Google Drive/HCP_graph/1200/5000perms/p_corrected.csv", sep=",", header=F)
row.names(img)<-c("<BMI",">BMI","<AoM",">AoM","<AoMxBMI",">AoMxBMI")
# head(img)
img_p<-1-img
# head(img_p)
bool<-img_p<0.05
img_p[img_p > 0.05] <- NASignificant positive BMI
5->7 == V67 5->10 == V70 9->11 == V131 10->13 == V148 14->12 == V207
matrix( as.matrix(img_p[1,1:225]),nrow=15, ncol=15)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] NA NA NA NA NA NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA 0.003 NA NA 0.012 NA NA
## [6,] NA NA NA NA NA NA NA NA NA NA NA NA
## [7,] NA NA NA NA 0.003 NA NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA NA NA NA 0.003 NA
## [10,] NA NA NA NA 0.012 NA NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA NA NA 0.003 NA NA NA
## [12,] NA NA NA NA NA NA NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA NA NA NA 0.002 NA NA
## [14,] NA NA NA NA NA NA NA NA NA NA NA 0.037
## [15,] NA NA NA NA NA NA NA NA NA NA NA NA
## [,13] [,14] [,15]
## [1,] NA NA NA
## [2,] NA NA NA
## [3,] NA NA NA
## [4,] NA NA NA
## [5,] NA NA NA
## [6,] NA NA NA
## [7,] NA NA NA
## [8,] NA NA NA
## [9,] NA NA NA
## [10,] 0.002 NA NA
## [11,] NA NA NA
## [12,] NA 0.037 NA
## [13,] NA NA NA
## [14,] NA NA NA
## [15,] NA NA NAp1<-ggplot(d4, aes(BMI, V67)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="5 and 7")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
p2<-ggplot(d4, aes(BMI, V70)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="5 and 10")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
p3<-ggplot(d4, aes(BMI, V131)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="9 and 11")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
p4<-ggplot(d4, aes(BMI, V148)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="10 and 13")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
ggarrange(p1,p2,p3,p4 + rremove("x.text"),
labels = c("A", "B", "C","D"),
ncol = 2, nrow = 2)
Significant negative BMI
1->6 == V6 3->4 == V34 4->8 == V53 4->15 == V60 10->12 == V147 10->15 == V150
matrix( as.matrix(img_p[2,1:225]),nrow=15, ncol=15)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] NA NA NA NA NA 0.044 NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA NA NA NA NA NA
## [3,] NA NA NA 0.046 NA NA NA NA NA NA NA NA
## [4,] NA NA 0.046 NA NA NA NA 0.016 NA NA NA NA
## [5,] NA NA NA NA NA NA NA NA NA NA NA NA
## [6,] 0.044 NA NA NA NA NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA NA NA NA NA NA
## [8,] NA NA NA 0.016 NA NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA NA NA NA NA NA 0.006
## [11,] NA NA NA NA NA NA NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA NA NA NA 0.006 NA NA
## [13,] NA NA NA NA NA NA NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA NA NA NA NA NA NA
## [15,] NA NA NA 0.002 NA NA NA NA NA 0.014 NA NA
## [,13] [,14] [,15]
## [1,] NA NA NA
## [2,] NA NA NA
## [3,] NA NA NA
## [4,] NA NA 0.002
## [5,] NA NA NA
## [6,] NA NA NA
## [7,] NA NA NA
## [8,] NA NA NA
## [9,] NA NA NA
## [10,] NA NA 0.014
## [11,] NA NA NA
## [12,] NA NA NA
## [13,] NA NA NA
## [14,] NA NA NA
## [15,] NA NA NAp1<-ggplot(d4, aes(BMI, V6)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="1 and 6")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
p2<-ggplot(d4, aes(BMI, V34)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="3 and 4")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
p3<-ggplot(d4, aes(BMI, V53)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="4 and 8")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
p4<-ggplot(d4, aes(BMI, V60)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="4 and 15")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
ggarrange(p1,p2,p3,p4 + rremove("x.text"),
labels = c("A", "B", "C","D"),
ncol = 2, nrow = 2)
Significant positive AoM
12->15 == V180
matrix( as.matrix(img_p[3,1:225]),nrow=15, ncol=15)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [15,] NA NA NA NA NA NA NA NA NA NA NA 0.048 NA
## [,14] [,15]
## [1,] NA NA
## [2,] NA NA
## [3,] NA NA
## [4,] NA NA
## [5,] NA NA
## [6,] NA NA
## [7,] NA NA
## [8,] NA NA
## [9,] NA NA
## [10,] NA NA
## [11,] NA NA
## [12,] NA 0.048
## [13,] NA NA
## [14,] NA NA
## [15,] NA NAp1<-ggplot(d4, aes(Menstrual_AgeBegan, V180)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="12 and 15")+
scale_x_continuous(name="AoM")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
p1
Significant negative AoM
none
matrix( as.matrix(img_p[4,1:225]),nrow=15, ncol=15)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [15,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [,14] [,15]
## [1,] NA NA
## [2,] NA NA
## [3,] NA NA
## [4,] NA NA
## [5,] NA NA
## [6,] NA NA
## [7,] NA NA
## [8,] NA NA
## [9,] NA NA
## [10,] NA NA
## [11,] NA NA
## [12,] NA NA
## [13,] NA NA
## [14,] NA NA
## [15,] NA NATertiles
So we can visualize the interaction effect
describe(d4$Menstrual_AgeBegan)## d4$Menstrual_AgeBegan
## n missing distinct Info Mean Gmd .05 .10
## 510 0 11 0.947 12.72 1.693 10.00 11.00
## .25 .50 .75 .90 .95
## 12.00 13.00 13.00 15.00 15.55
##
## Value 8 9 10 11 12 13 14 15 16 17
## Frequency 2 12 19 54 147 151 64 35 17 5
## Proportion 0.004 0.024 0.037 0.106 0.288 0.296 0.125 0.069 0.033 0.010
##
## Value 18
## Frequency 4
## Proportion 0.008hist(d4$Menstrual_AgeBegan)
quantile(d4$Menstrual_AgeBegan, prob = c(0.33, 0.66))## 33% 66%
## 12 13d4$AoM[d4$Menstrual_AgeBegan<12]<-"early"
d4$AoM[d4$Menstrual_AgeBegan>13]<-"late"
summary(as.factor(d4$AoM))## early late NA's
## 87 125 298Significant positive AoMxBMI
12 (visual cortex)->13 (somatosensory - mouth) == V178
IC 12
IC 13
3 (visual) -> 6 (cingulo-opercular) == V36
IC 3
IC 6
matrix( as.matrix(img_p[5,1:225]),nrow=15, ncol=15)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA 0 NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6,] NA NA 0 NA NA NA NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA NA NA NA NA NA NA 0.026
## [13,] NA NA NA NA NA NA NA NA NA NA NA 0.026 NA
## [14,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [15,] NA NA NA NA NA NA NA NA NA NA NA NA NA
## [,14] [,15]
## [1,] NA NA
## [2,] NA NA
## [3,] NA NA
## [4,] NA NA
## [5,] NA NA
## [6,] NA NA
## [7,] NA NA
## [8,] NA NA
## [9,] NA NA
## [10,] NA NA
## [11,] NA NA
## [12,] NA NA
## [13,] NA NA
## [14,] NA NA
## [15,] NA NAn=50
p1<-ggplot(subset(d4, !is.na(d4$AoM)), aes(BMI, V36, group=AoM, color=AoM)) +
geom_point(aes(shape=AoM), size=2) +
geom_smooth(method=lm, aes(linetype=AoM))+
scale_y_continuous(name="Correlation between\n IC3 and IC6")+
scale_x_continuous(name="BMI")+
theme_classic()+
scale_shape_manual(values = c(1, 2),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
scale_color_manual(values = c("black", "#333333"),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
scale_linetype_manual(values=c("solid","dashed"),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
theme(axis.title.x = element_text(size=20), axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text(size=20), axis.text.y = element_text(size=20))+
theme(legend.text = element_text(size=20))+
theme(legend.title = element_text(size=20, face="bold"))
p2<-ggplot(subset(d4, !is.na(d4$AoM)), aes(BMI, V178, group=AoM, color=AoM)) +
geom_point(aes(shape=AoM), size=2) +
geom_smooth(method=lm, aes(linetype=AoM))+
scale_y_continuous(name="Correlation between\n IC12 and IC13")+
scale_x_continuous(name="BMI")+
theme_classic()+
scale_shape_manual(values = c(1, 2),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
scale_color_manual(values = c("black", "#333333"),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
scale_linetype_manual(values=c("solid","dashed"),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
theme(axis.title.x = element_text(size=20), axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text(size=20), axis.text.y = element_text(size=20))+
theme(legend.text = element_text(size=20))+
theme(legend.title = element_text(size=20, face="bold"))
interaction_plot<- ggarrange(p1,p2 ,
labels = c("A", "B"),
ncol = 2, nrow = 1)
interaction_plot
Significant negative AoMxBMI
2 (DMN) ->3 (visual) == V18 ### IC 2 
IC 3
matrix( as.matrix(img_p[6,1:225]),nrow=15, ncol=15)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] NA NA NA NA NA NA NA NA NA NA NA NA
## [2,] NA NA 0.004 NA NA NA NA NA NA NA NA NA
## [3,] NA 0.004 NA NA NA NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA NA NA NA NA NA NA
## [15,] NA NA NA NA NA NA NA NA NA NA NA NA
## [,13] [,14] [,15]
## [1,] NA NA NA
## [2,] NA NA NA
## [3,] NA NA NA
## [4,] NA NA NA
## [5,] NA NA NA
## [6,] NA NA NA
## [7,] NA NA NA
## [8,] NA NA NA
## [9,] NA NA NA
## [10,] NA NA NA
## [11,] NA NA NA
## [12,] NA NA NA
## [13,] NA NA NA
## [14,] NA NA NA
## [15,] NA NA NAp3<-ggplot(subset(d4, !is.na(d4$AoM)), aes(BMI, V18, group=AoM, color=AoM)) +
geom_point(aes(shape=AoM), size=2) +
geom_smooth(method=lm, aes(linetype=AoM))+
scale_y_continuous(name="Correlation between\n IC2 and IC3")+
scale_x_continuous(name="BMI")+
theme_classic()+
scale_shape_manual(values = c(1, 2),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
scale_color_manual(values = c("black", "#333333"),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
scale_linetype_manual(values=c("solid","dashed"),
breaks = c("early","late"),
labels = c("Early onsent (<12y)", "Late onsent (>13y)"),
guide = guide_legend(title.position = "top", title = "Age at onset of menses", size=20))+
theme(axis.title.x = element_text(size=20), axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text(size=20), axis.text.y = element_text(size=20))+
theme(legend.text = element_text(size=20))+
theme(legend.title = element_text(size=20, face="bold"))
p3
Post hoc analysis of HCP data for puberty paper
Checking the fertility data
summary(as.factor(d4$Menstrual_UsingBirthControl))## 0 1
## 367 143# yes = 1
onBC<-subset(d4, d4$Menstrual_UsingBirthControl == "1")
summary(as.factor(onBC$Menstrual_UsingBirthControl))## 1
## 143summary(as.factor(onBC$Menstrual_BirthControlCode))## 1 2 3 4 6 NA's
## 95 29 1 8 9 1# 1=OC's for contraception, 2=OC's primarily for menstrual regulation, 3=estradiol for menstrual regulation, 4=progesterone for menstrual regulation, 5=fertility therapy, 6=other, 7=unknown (Asked of female participants only) Checking SES
# SSAGA_Employ SSAGA_Income SSAGA_Educ SSAGA_InSchool SSAGA_Rlshp
## not working = 0, part-time employment = 1; full-time employment = 2
summary(as.factor(d4$SSAGA_Employ))## 0 1 2
## 92 105 313## Total household income: <$10,000 = 1,10K-19,999 = 2, 20K-29,999 = 3,30K-39,999 = 4, 40K-49,999 = 5, 50K-74,999 = 6, 75K-99,999 = 7, >=100,000 = 8
## Low income == 1-2
summary(as.factor(d4$SSAGA_Income))## 1 2 3 4 5 6 7 8 NA's
## 39 29 55 67 56 105 77 79 3## Years of education completed: <11 = 11; 12; 13; 14; 15; 16; 17+ = 17
summary(as.factor(d4$SSAGA_Educ))## 11 12 13 14 15 16 17
## 16 69 26 63 19 226 91## Is respondent still in school for degree course? no = 0; yes = 1
summary(as.factor(d4$SSAGA_InSchool))## 0 1
## 419 91## Is respondent married or in live-in relationship? no = 0; yes = 1
summary(as.factor(d4$SSAGA_Rlshp))## 0 1
## 252 258low_income_single<-subset(d4 , d4$SSAGA_InSchool == 0 & d4$SSAGA_Rlshp == 0 & d4$SSAGA_Income %in% c('1', '2'))
low_income_married<-subset(d4 , d4$SSAGA_InSchool == 0 & d4$SSAGA_Rlshp == 1 & d4$SSAGA_Income %in% c('1', '2','3'))
low<-rbind(low_income_married, low_income_single)
d4$SES[d4$SSAGA_InSchool == 0 & d4$SSAGA_Rlshp == 0 & d4$SSAGA_Income %in% c('1', '2')]<-"low_single"
d4$SES[d4$SSAGA_InSchool == 0 & d4$SSAGA_Rlshp == 1 & d4$SSAGA_Income %in% c('1', '2','3')]<-"low_married"
d4$SES[d4$SSAGA_InSchool == 0 & d4$SSAGA_Rlshp == 0 & d4$SSAGA_Income %in% c('3','4','5','6','7','8')]<-"norm_single"
d4$SES[d4$SSAGA_InSchool == 0 & d4$SSAGA_Rlshp == 1 & d4$SSAGA_Income %in% c('4','5','6','7','8')]<-"norm_married"
d4$SES[d4$SSAGA_InSchool == 1 & d4$SSAGA_Rlshp == 0 & d4$SSAGA_Income %in% c('1', '2')]<-"low_single_school"
d4$SES[d4$SSAGA_InSchool == 1 & d4$SSAGA_Rlshp == 1 & d4$SSAGA_Income %in% c('1', '2','3')]<-"low_married_school"
d4$SES[d4$SSAGA_InSchool == 1 & d4$SSAGA_Rlshp == 0 & d4$SSAGA_Income %in% c('3','4','5','6','7','8')]<-"norm_single_school"
d4$SES[d4$SSAGA_InSchool == 1 & d4$SSAGA_Rlshp == 1 & d4$SSAGA_Income %in% c('4','5','6','7','8')]<-"norm_married_school"
d4$SES_comp[d4$SES %in% c('low_single', 'low_married','low_single_school','low_married_school')]<-"low"
d4$SES_comp[d4$SES %in% c('norm_single', 'norm_married','norm_single_school','norm_married_school')]<-"norm"Defintition of low income
If single: THI < 20K If married: THI <30K
ggplot(subset(d4, d4$SES_comp != is.na(d4$SES_comp)), aes(Menstrual_AgeBegan, BMI, group=SES_comp, color=SES_comp)) +
geom_point(shape=1) +
geom_smooth(method=lm)+theme_classic()+scale_y_continuous(name="Body Mass Index (BMI)")+
scale_x_continuous(name="Age of onset of menstration")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))
d4$SES_comp <- factor(d4$SES_comp)
summary(d4$SES_comp)## low norm NA's
## 76 431 3mylogit <- glm(SES_comp ~ Menstrual_AgeBegan*BMI, data = subset(d4, d4$SES_comp != is.na(d4$SES_comp)), family = "binomial")
summary(mylogit)##
## Call:
## glm(formula = SES_comp ~ Menstrual_AgeBegan * BMI, family = "binomial",
## data = subset(d4, d4$SES_comp != is.na(d4$SES_comp)))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2908 0.4422 0.5326 0.5909 1.0297
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.122846 4.890894 0.434 0.664
## Menstrual_AgeBegan 0.006335 0.390246 0.016 0.987
## BMI -0.119432 0.176673 -0.676 0.499
## Menstrual_AgeBegan:BMI 0.008158 0.014238 0.573 0.567
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 428.45 on 506 degrees of freedom
## Residual deviance: 418.88 on 503 degrees of freedom
## AIC: 426.88
##
## Number of Fisher Scoring iterations: 4Nodes of interest
sig 3->6 == V36 check 6 -> 3 == V78 12->13 == V192 check 13->12 == V178
Control variables
Race Ethnicity Age_in_Yrs
ctrl1<-lm(BMI~Race+Ethnicity+Age_in_Yrs, data=d4)
p.adjust(coef(summary(ctrl1))[, 4], method="BH")## (Intercept)
## 2.417328e-23
## RaceAm. Indian/Alaskan Nat.
## 6.374647e-01
## RaceAsian/Nat. Hawaiian/Othr Pacific Is.
## 2.501776e-03
## RaceBlack or African Am.
## 2.652613e-07
## RaceMore than one
## 8.677432e-01
## RaceUnknown or Not Reported
## 2.559852e-01
## EthnicityNot Hispanic/Latino
## 1.695358e-01
## EthnicityUnknown or Not Reported
## 1.695358e-01
## Age_in_Yrs
## 1.412720e-01ctrl2<-lm(Menstrual_AgeBegan~Race+Ethnicity+Age_in_Yrs, data=d4)
p.adjust(coef(summary(ctrl2))[, 4], method="BH")## (Intercept)
## 6.742451e-60
## RaceAm. Indian/Alaskan Nat.
## 1.118843e-01
## RaceAsian/Nat. Hawaiian/Othr Pacific Is.
## 1.890923e-01
## RaceBlack or African Am.
## 4.124785e-02
## RaceMore than one
## 8.690411e-01
## RaceUnknown or Not Reported
## 9.947862e-01
## EthnicityNot Hispanic/Latino
## 1.118843e-01
## EthnicityUnknown or Not Reported
## 9.998632e-01
## Age_in_Yrs
## 8.690411e-01twoM <- rbind(tidy(ctrl1) %>% mutate(model = "Relationshio to BMI"),
tidy(ctrl2) %>% mutate(model = "Relationship to age at onset of menses")
)
SI1<-dwplot(twoM, vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2)) +theme_classic()
SI1
Looks like I can drop ethnicity since it is not related to either BMI nor Puberty.
False Discovery Correction
Bonferroni Cor #: 11
ad = 11Delay Discounting: Area Under the Curve for Discounting of $200 (DDisc_AUC_200) Cognition Self-regulation/Impulsivity (Delay Discounting) DDisc_AUC_200
mDD1<-lm(DDisc_AUC_200~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mDD1)##
## Call:
## lm(formula = DDisc_AUC_200 ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33258 -0.12193 -0.04144 0.08120 0.73830
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.8274071 0.3302516 2.505
## BMI -0.0197156 0.0119813 -1.646
## Menstrual_AgeBegan -0.0469446 0.0248398 -1.890
## RaceAm. Indian/Alaskan Nat. -0.2004296 0.1833454 -1.093
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.0638199 0.0352343 1.811
## RaceBlack or African Am. -0.1127362 0.0233714 -4.824
## RaceMore than one 0.1369166 0.0539355 2.539
## RaceUnknown or Not Reported -0.0680833 0.0585539 -1.163
## Age_in_Yrs 0.0015771 0.0023263 0.678
## BMI:Menstrual_AgeBegan 0.0015134 0.0009354 1.618
## Pr(>|t|)
## (Intercept) 0.0126 *
## BMI 0.1005
## Menstrual_AgeBegan 0.0594 .
## RaceAm. Indian/Alaskan Nat. 0.2748
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.0707 .
## RaceBlack or African Am. 1.88e-06 ***
## RaceMore than one 0.0114 *
## RaceUnknown or Not Reported 0.2455
## Age_in_Yrs 0.4981
## BMI:Menstrual_AgeBegan 0.1063
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1816 on 497 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.08571, Adjusted R-squared: 0.06915
## F-statistic: 5.177 on 9 and 497 DF, p-value: 9.86e-07Delay Discounting: Area Under the Curve for Discounting of $40,000 (DDisc_AUC_40K) Cognition Self-regulation/Impulsivity (Delay Discounting) DDisc_AUC_40K
mDD2<-lm(DDisc_AUC_40K~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mDD2)#trend##
## Call:
## lm(formula = DDisc_AUC_40K ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.57488 -0.22144 -0.00278 0.23481 0.60842
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.8160111 0.5001540 3.631
## BMI -0.0470355 0.0181453 -2.592
## Menstrual_AgeBegan -0.0941783 0.0376190 -2.503
## RaceAm. Indian/Alaskan Nat. -0.4497669 0.2776700 -1.620
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.0676138 0.0533610 1.267
## RaceBlack or African Am. -0.1692236 0.0353951 -4.781
## RaceMore than one 0.1326965 0.0816834 1.625
## RaceUnknown or Not Reported -0.2453774 0.0886778 -2.767
## Age_in_Yrs 0.0002894 0.0035230 0.082
## BMI:Menstrual_AgeBegan 0.0034075 0.0014167 2.405
## Pr(>|t|)
## (Intercept) 0.000312 ***
## BMI 0.009818 **
## Menstrual_AgeBegan 0.012618 *
## RaceAm. Indian/Alaskan Nat. 0.105912
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.205713
## RaceBlack or African Am. 2.3e-06 ***
## RaceMore than one 0.104899
## RaceUnknown or Not Reported 0.005867 **
## Age_in_Yrs 0.934558
## BMI:Menstrual_AgeBegan 0.016526 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.275 on 497 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1021, Adjusted R-squared: 0.08587
## F-statistic: 6.281 on 9 and 497 DF, p-value: 1.959e-08p.adjust(coef(summary(mDD2))[, 4], method="BH", ad)## (Intercept)
## 1.714493e-03
## BMI
## 2.699981e-02
## Menstrual_AgeBegan
## 2.775892e-02
## RaceAm. Indian/Alaskan Nat.
## 1.456288e-01
## RaceAsian/Nat. Hawaiian/Othr Pacific Is.
## 2.514268e-01
## RaceBlack or African Am.
## 2.531508e-05
## RaceMore than one
## 1.456288e-01
## RaceUnknown or Not Reported
## 2.151296e-02
## Age_in_Yrs
## 1.000000e+00
## BMI:Menstrual_AgeBegan
## 3.029715e-02NIH Toolbox Picture Sequence Memory Test: Age-Adjusted Scale Score
mSM1<-lm(PicSeq_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mSM1)##
## Call:
## lm(formula = PicSeq_AgeAdj ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.25 -10.11 0.15 10.67 36.71
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 112.96597 27.32408 4.134
## BMI -0.63352 0.99091 -0.639
## Menstrual_AgeBegan -0.49635 2.05488 -0.242
## RaceAm. Indian/Alaskan Nat. 0.99211 15.17770 0.065
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 7.96689 2.83555 2.810
## RaceBlack or African Am. -9.16232 1.93450 -4.736
## RaceMore than one 3.83875 4.46468 0.860
## RaceUnknown or Not Reported 7.93112 4.84734 1.636
## Age_in_Yrs 0.30484 0.19201 1.588
## BMI:Menstrual_AgeBegan 0.02810 0.07736 0.363
## Pr(>|t|)
## (Intercept) 4.18e-05 ***
## BMI 0.52290
## Menstrual_AgeBegan 0.80923
## RaceAm. Indian/Alaskan Nat. 0.94791
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.00515 **
## RaceBlack or African Am. 2.84e-06 ***
## RaceMore than one 0.39031
## RaceUnknown or Not Reported 0.10243
## Age_in_Yrs 0.11300
## BMI:Menstrual_AgeBegan 0.71657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.03 on 500 degrees of freedom
## Multiple R-squared: 0.0998, Adjusted R-squared: 0.0836
## F-statistic: 6.159 on 9 and 500 DF, p-value: 3.01e-08NIH Toolbox Dimensional Change Card Sort Test: Age-Adjusted Scale Score
mCS1<-lm(CardSort_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mCS1)##
## Call:
## lm(formula = CardSort_AgeAdj ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.137 -6.555 0.020 7.097 21.667
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 105.01522 17.87378 5.875
## BMI -0.08726 0.64772 -0.135
## Menstrual_AgeBegan 0.34168 1.34359 0.254
## RaceAm. Indian/Alaskan Nat. 2.92154 9.91813 0.295
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. -1.36425 1.85285 -0.736
## RaceBlack or African Am. -1.34244 1.26429 -1.062
## RaceMore than one 0.23302 2.91737 0.080
## RaceUnknown or Not Reported -1.67815 3.16742 -0.530
## Age_in_Yrs 0.03995 0.12598 0.317
## BMI:Menstrual_AgeBegan -0.01816 0.05057 -0.359
## Pr(>|t|)
## (Intercept) 7.73e-09 ***
## BMI 0.893
## Menstrual_AgeBegan 0.799
## RaceAm. Indian/Alaskan Nat. 0.768
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.462
## RaceBlack or African Am. 0.289
## RaceMore than one 0.936
## RaceUnknown or Not Reported 0.596
## Age_in_Yrs 0.751
## BMI:Menstrual_AgeBegan 0.720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.821 on 498 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03724, Adjusted R-squared: 0.01984
## F-statistic: 2.14 on 9 and 498 DF, p-value: 0.02493NIH Toolbox Flanker Inhibitory Control and Attention Test: Age-Adjusted Scale Score
mFl1<-lm(Flanker_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mFl1)##
## Call:
## lm(formula = Flanker_AgeAdj ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.7445 -6.2980 0.1627 6.9671 22.3189
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 101.55915 17.18122 5.911
## BMI 0.09309 0.62308 0.149
## Menstrual_AgeBegan 0.25581 1.29209 0.198
## RaceAm. Indian/Alaskan Nat. 11.03254 9.54364 1.156
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.14332 1.78298 0.080
## RaceBlack or African Am. -2.64538 1.21640 -2.175
## RaceMore than one 2.45298 2.80737 0.874
## RaceUnknown or Not Reported -3.12606 3.04798 -1.026
## Age_in_Yrs -0.09421 0.12073 -0.780
## BMI:Menstrual_AgeBegan -0.00949 0.04864 -0.195
## Pr(>|t|)
## (Intercept) 6.3e-09 ***
## BMI 0.8813
## Menstrual_AgeBegan 0.8431
## RaceAm. Indian/Alaskan Nat. 0.2482
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.9360
## RaceBlack or African Am. 0.0301 *
## RaceMore than one 0.3827
## RaceUnknown or Not Reported 0.3056
## Age_in_Yrs 0.4356
## BMI:Menstrual_AgeBegan 0.8454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.451 on 500 degrees of freedom
## Multiple R-squared: 0.0194, Adjusted R-squared: 0.00175
## F-statistic: 1.099 on 9 and 500 DF, p-value: 0.3616NIH Toolbox Cognition Fluid Composite: Age Adjusted Scale Score
mCF1<-lm(CogFluidComp_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mCF1)##
## Call:
## lm(formula = CogFluidComp_AgeAdj ~ BMI * Menstrual_AgeBegan +
## Race + Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.494 -11.829 0.058 11.230 45.417
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 120.17028 30.45998 3.945
## BMI -0.69154 1.11400 -0.621
## Menstrual_AgeBegan -0.60699 2.28751 -0.265
## RaceAm. Indian/Alaskan Nat. -5.40172 16.24106 -0.333
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 4.33877 3.03543 1.429
## RaceBlack or African Am. -9.42034 2.07478 -4.540
## RaceMore than one 4.71302 4.77730 0.987
## RaceUnknown or Not Reported -0.54191 5.18652 -0.104
## Age_in_Yrs 0.15784 0.20695 0.763
## BMI:Menstrual_AgeBegan 0.02389 0.08672 0.275
## Pr(>|t|)
## (Intercept) 9.13e-05 ***
## BMI 0.535
## Menstrual_AgeBegan 0.791
## RaceAm. Indian/Alaskan Nat. 0.740
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.154
## RaceBlack or African Am. 7.06e-06 ***
## RaceMore than one 0.324
## RaceUnknown or Not Reported 0.917
## Age_in_Yrs 0.446
## BMI:Menstrual_AgeBegan 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.08 on 493 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.08416, Adjusted R-squared: 0.06744
## F-statistic: 5.034 on 9 and 493 DF, p-value: 1.641e-06NIH Toolbox Cognition Total Composite Score: Age Adjusted Scale Score
mCCom1<-lm(CogTotalComp_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mCCom1)##
## Call:
## lm(formula = CogTotalComp_AgeAdj ~ BMI * Menstrual_AgeBegan +
## Race + Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.751 -11.976 -0.547 12.376 56.076
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 185.02935 34.95169 5.294
## BMI -2.03710 1.27827 -1.594
## Menstrual_AgeBegan -3.95099 2.62483 -1.505
## RaceAm. Indian/Alaskan Nat. -20.72131 18.63601 -1.112
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 10.33954 3.48304 2.969
## RaceBlack or African Am. -16.91864 2.38073 -7.107
## RaceMore than one 4.07736 5.48178 0.744
## RaceUnknown or Not Reported -10.71278 5.95134 -1.800
## Age_in_Yrs -0.22316 0.23747 -0.940
## BMI:Menstrual_AgeBegan 0.11761 0.09951 1.182
## Pr(>|t|)
## (Intercept) 1.81e-07 ***
## BMI 0.11166
## Menstrual_AgeBegan 0.13290
## RaceAm. Indian/Alaskan Nat. 0.26672
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.00314 **
## RaceBlack or African Am. 4.19e-12 ***
## RaceMore than one 0.45735
## RaceUnknown or Not Reported 0.07246 .
## Age_in_Yrs 0.34781
## BMI:Menstrual_AgeBegan 0.23781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.45 on 493 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1841, Adjusted R-squared: 0.1692
## F-statistic: 12.36 on 9 and 493 DF, p-value: < 2.2e-16NIH Toolbox Cognition Crystallized Composite: Age Adjusted Scale Score
About
The Crystallized Cognition Composite score is derived by averaging the normalized scores of each of the Toolbox tests that are crystallized measures (Picture Vocabulary and Reading Tests), then deriving scale scores based on this new distribution. One can interpret the Crystallized Cognition Composite as a more global assessment of individual and group verbal reasoning. Higher scores indicate higher levels of functioning. Age-adjusted Scale Score: Participant score is normed using the age appropriate band of Toolbox Norming Sample (bands of ages 18-29, or 30-35), where a score of 100 indicates performance that was at the national average and a score of 115 or 85, indicates performance 1 SD above or below the national average for participants age band.
mCCr1<-lm(CogCrystalComp_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mCCr1) #SIGNIFICANT##
## Call:
## lm(formula = CogCrystalComp_AgeAdj ~ BMI * Menstrual_AgeBegan +
## Race + Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.274 -11.104 0.612 10.906 41.016
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 188.54764 28.62606 6.587
## BMI -2.13047 1.04783 -2.033
## Menstrual_AgeBegan -4.88907 2.15105 -2.273
## RaceAm. Indian/Alaskan Nat. -23.81526 15.28267 -1.558
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 10.05298 2.85552 3.521
## RaceBlack or African Am. -14.68716 1.95114 -7.527
## RaceMore than one 0.58530 4.49483 0.130
## RaceUnknown or Not Reported -14.68602 4.88015 -3.009
## Age_in_Yrs -0.23906 0.19374 -1.234
## BMI:Menstrual_AgeBegan 0.13882 0.08155 1.702
## Pr(>|t|)
## (Intercept) 1.15e-10 ***
## BMI 0.04256 *
## Menstrual_AgeBegan 0.02346 *
## RaceAm. Indian/Alaskan Nat. 0.11979
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.00047 ***
## RaceBlack or African Am. 2.45e-13 ***
## RaceMore than one 0.89645
## RaceUnknown or Not Reported 0.00275 **
## Age_in_Yrs 0.21782
## BMI:Menstrual_AgeBegan 0.08933 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.13 on 497 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2071, Adjusted R-squared: 0.1927
## F-statistic: 14.42 on 9 and 497 DF, p-value: < 2.2e-16### it appears that race only improves the model for Asian therefore we will test this without the asian population which is low (36)
p.adjust(coef(summary(mCCr1))[, 4], method="BH", ad)## (Intercept)
## 6.314609e-10
## BMI
## 7.802776e-02
## Menstrual_AgeBegan
## 5.161079e-02
## RaceAm. Indian/Alaskan Nat.
## 1.647171e-01
## RaceAsian/Nat. Hawaiian/Othr Pacific Is.
## 1.724372e-03
## RaceBlack or African Am.
## 2.695411e-12
## RaceMore than one
## 9.860929e-01
## RaceUnknown or Not Reported
## 7.565694e-03
## Age_in_Yrs
## 2.662187e-01
## BMI:Menstrual_AgeBegan
## 1.403815e-01ggplot(subset(d4, !is.na(AoM)), aes(BMI, CogCrystalComp_AgeAdj, color=AoM, group=AoM)) +
geom_point(shape=1) +
geom_smooth(method=lm)+scale_y_continuous(name="crystal cognition")+
scale_x_continuous(name="BMI")+
theme(axis.title.x = element_text( size=20),axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text( size=20),axis.text.y = element_text(size=20))## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# HbA1c
mHb1<-lm(HbA1C~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mHb1)##
## Call:
## lm(formula = HbA1C ~ BMI * Menstrual_AgeBegan + Race + Age_in_Yrs,
## data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8576 -0.1744 0.0388 0.1848 0.9460
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.650516 0.887597 5.239
## BMI 0.021687 0.032463 0.668
## Menstrual_AgeBegan 0.020814 0.066581 0.313
## RaceAm. Indian/Alaskan Nat. 0.724743 0.366525 1.977
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.033654 0.084805 0.397
## RaceBlack or African Am. 0.166618 0.061866 2.693
## RaceMore than one 0.204308 0.131077 1.559
## RaceUnknown or Not Reported 0.068296 0.131327 0.520
## Age_in_Yrs 0.009134 0.005763 1.585
## BMI:Menstrual_AgeBegan -0.001629 0.002522 -0.646
## Pr(>|t|)
## (Intercept) 2.94e-07 ***
## BMI 0.50459
## Menstrual_AgeBegan 0.75479
## RaceAm. Indian/Alaskan Nat. 0.04887 *
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.69176
## RaceBlack or African Am. 0.00745 **
## RaceMore than one 0.12007
## RaceUnknown or Not Reported 0.60339
## Age_in_Yrs 0.11399
## BMI:Menstrual_AgeBegan 0.51877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3602 on 317 degrees of freedom
## (183 observations deleted due to missingness)
## Multiple R-squared: 0.06124, Adjusted R-squared: 0.03459
## F-statistic: 2.298 on 9 and 317 DF, p-value: 0.01642NIH Toolbox 2-minute Walk Endurance Test : Age-Adjusted Scale Score Motor Endurance (2 minute walk test) Endurance_AgeAdj
mEn1<-lm(Endurance_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mEn1)##
## Call:
## lm(formula = Endurance_AgeAdj ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.356 -7.474 0.017 7.837 29.801
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 91.83503 19.78930 4.641
## BMI -0.39292 0.71763 -0.548
## Menstrual_AgeBegan 1.15945 1.48816 0.779
## RaceAm. Indian/Alaskan Nat. -10.45314 10.99178 -0.951
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 1.11517 2.05390 0.543
## RaceBlack or African Am. -5.70515 1.40156 -4.071
## RaceMore than one -2.14498 3.23369 -0.663
## RaceUnknown or Not Reported -4.81137 3.51079 -1.370
## Age_in_Yrs 0.66976 0.13915 4.813
## BMI:Menstrual_AgeBegan -0.03278 0.05602 -0.585
## Pr(>|t|)
## (Intercept) 4.44e-06 ***
## BMI 0.584
## Menstrual_AgeBegan 0.436
## RaceAm. Indian/Alaskan Nat. 0.342
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.587
## RaceBlack or African Am. 5.45e-05 ***
## RaceMore than one 0.507
## RaceUnknown or Not Reported 0.171
## Age_in_Yrs 1.97e-06 ***
## BMI:Menstrual_AgeBegan 0.559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.89 on 499 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2352, Adjusted R-squared: 0.2214
## F-statistic: 17.05 on 9 and 499 DF, p-value: < 2.2e-16NIH Toolbox Grip Strength Test: Age-Adjusted-Adjusted Scale Score Motor Strength (Grip Strength Dynamometry) Strength_AgeAdj
mSt1<-lm(Strength_AgeAdj ~ BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mSt1)##
## Call:
## lm(formula = Strength_AgeAdj ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.312 -7.655 -0.642 6.098 32.964
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 35.76141 20.84647 1.715
## BMI 1.40293 0.75600 1.856
## Menstrual_AgeBegan 2.56273 1.56774 1.635
## RaceAm. Indian/Alaskan Nat. 13.24996 11.57958 1.144
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. -2.61721 2.16334 -1.210
## RaceBlack or African Am. 4.04648 1.47590 2.742
## RaceMore than one 0.17506 3.40626 0.051
## RaceUnknown or Not Reported -0.74205 3.69820 -0.201
## Age_in_Yrs 0.46774 0.14649 3.193
## BMI:Menstrual_AgeBegan -0.08986 0.05902 -1.522
## Pr(>|t|)
## (Intercept) 0.08688 .
## BMI 0.06408 .
## Menstrual_AgeBegan 0.10275
## RaceAm. Indian/Alaskan Nat. 0.25307
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.22693
## RaceBlack or African Am. 0.00633 **
## RaceMore than one 0.95903
## RaceUnknown or Not Reported 0.84105
## Age_in_Yrs 0.00150 **
## BMI:Menstrual_AgeBegan 0.12852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.47 on 500 degrees of freedom
## Multiple R-squared: 0.08035, Adjusted R-squared: 0.0638
## F-statistic: 4.854 on 9 and 500 DF, p-value: 3.057e-06NIH Toolbox Perceived Stress Survey: Unadjusted Scale Score Emotion Stress and Self Efficacy (Perceived Stress, Self-Efficacy) PercStress_Unadj
mStress1<-lm(PercStress_Unadj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mStress1)##
## Call:
## lm(formula = PercStress_Unadj ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.9184 -5.4434 -0.2983 4.9690 30.1910
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 60.35576 16.05213 3.760
## BMI -0.07033 0.58202 -0.121
## Menstrual_AgeBegan -0.60687 1.20676 -0.503
## RaceAm. Indian/Alaskan Nat. 10.72355 8.91277 1.203
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.24631 1.66542 0.148
## RaceBlack or African Am. 1.32934 1.14323 1.163
## RaceMore than one 0.92817 2.62189 0.354
## RaceUnknown or Not Reported 2.05733 2.84648 0.723
## Age_in_Yrs -0.23207 0.11292 -2.055
## BMI:Menstrual_AgeBegan 0.01349 0.04543 0.297
## Pr(>|t|)
## (Intercept) 0.00019 ***
## BMI 0.90387
## Menstrual_AgeBegan 0.61526
## RaceAm. Indian/Alaskan Nat. 0.22948
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.88249
## RaceBlack or African Am. 0.24547
## RaceMore than one 0.72348
## RaceUnknown or Not Reported 0.47016
## Age_in_Yrs 0.04039 *
## BMI:Menstrual_AgeBegan 0.76666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.826 on 499 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02416, Adjusted R-squared: 0.006555
## F-statistic: 1.372 on 9 and 499 DF, p-value: 0.1976Taste_AgeAdj
mTaste1<-lm(Taste_AgeAdj~BMI*Menstrual_AgeBegan+Race+Age_in_Yrs, data = d4)
summary(mTaste1)##
## Call:
## lm(formula = Taste_AgeAdj ~ BMI * Menstrual_AgeBegan + Race +
## Age_in_Yrs, data = d4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.946 -10.579 0.442 8.924 34.713
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 51.49530 25.72285 2.002
## BMI 1.48828 0.93253 1.596
## Menstrual_AgeBegan 2.75580 1.93366 1.425
## RaceAm. Indian/Alaskan Nat. -15.08900 14.28005 -1.057
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 3.34109 2.70026 1.237
## RaceBlack or African Am. 3.36626 1.84077 1.829
## RaceMore than one 1.69588 4.20101 0.404
## RaceUnknown or Not Reported 13.26855 5.08144 2.611
## Age_in_Yrs 0.12685 0.18139 0.699
## BMI:Menstrual_AgeBegan -0.10341 0.07279 -1.421
## Pr(>|t|)
## (Intercept) 0.0458 *
## BMI 0.1111
## Menstrual_AgeBegan 0.1547
## RaceAm. Indian/Alaskan Nat. 0.2912
## RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.2166
## RaceBlack or African Am. 0.0680 .
## RaceMore than one 0.6866
## RaceUnknown or Not Reported 0.0093 **
## Age_in_Yrs 0.4847
## BMI:Menstrual_AgeBegan 0.1561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.14 on 495 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.03516, Adjusted R-squared: 0.01761
## F-statistic: 2.004 on 9 and 495 DF, p-value: 0.03707Heritability
Self-reported zygosity. Until the S1200 release this was the " measure. The small number of subjects who do not have a value for this measure (blank) self reported as twins but did not self report their twin zygosity."
Twin zygosity verified by genotyping. Requires both subjects in a twin pair to have HasGT=TRUE to have a value (if genotyping is not available for either of a twin pair, no values are given for ZygosityGT). Non-twin subjects also do not have a value for ZygosityGT. Note that some subjects self-reported as dizygotic twins (ZygositySR=‘NotMZ’) but genotyping established that they were monozygotic twins (thence ZygosityGT=‘MZ’ for those subjects). ZygosityGT should be given precedence over ZygositySR.
Nodes of interest
sig 3->6 == V36 check 6 -> 3 == V78 12->13 == V192 check 13->12 == V178
twins<-subset(d4 , d4$ZygosityGT %in% c('DZ', 'MZ'))
myvars<-c("Family_ID","Subject","Menstrual_AgeBegan","BMI","ZygosityGT","Age_in_Yrs","Race","Gender","V36","V78","V178","V192")
d5<-twins[myvars]
d5$ZygosityGT<-factor(d5$ZygosityGT)
d5$Race<-factor(d5$Race)
d6 <- fast.reshape(d5, id="Family_ID",varying=c("BMI","Menstrual_AgeBegan","Subject","V36","V78","V178","V192"))
d6<-na.omit(d6)
head(d6)## Family_ID Subject1 Menstrual_AgeBegan1 BMI1 ZygosityGT Age_in_Yrs
## 223 51279_81145 139637 12 24.95 MZ 35
## 857 51293_81159 552544 11 28.66 DZ 35
## 488 51295_81161 191437 11 44.70 DZ 35
## 150 51300_81166 127832 15 20.72 DZ 35
## 319 51303_81168 157336 13 22.27 DZ 34
## 141 51304_81169 125525 14 21.46 MZ 34
## Race Gender V361 V781 V1781 V1921 BMI2
## 223 White F -2.00540 -2.00540 16.3360 16.3360 28.20
## 857 White F 0.63242 0.63242 5.3064 5.3064 19.37
## 488 White F -2.49730 -2.49730 -4.3874 -4.3874 34.50
## 150 White F -5.63610 -5.63610 8.9695 8.9695 23.69
## 319 White F -2.40650 -2.40650 4.7688 4.7688 20.99
## 141 White F 5.30000 5.30000 5.9922 5.9922 20.01
## Menstrual_AgeBegan2 Subject2 V362 V782 V1782 V1922
## 223 15 677968 -3.64770 -3.64770 10.15000 10.15000
## 857 12 887373 0.73871 0.73871 11.19500 11.19500
## 488 13 559053 -0.60196 -0.60196 -2.67200 -2.67200
## 150 12 137431 -4.62170 -4.62170 0.68453 0.68453
## 319 13 429040 -0.50878 -0.50878 6.30830 6.30830
## 141 14 192439 -0.81324 -0.81324 2.76340 2.76340library("cowplot") ##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************##
## Attaching package: 'cowplot'## The following object is masked from 'package:mosaic':
##
## theme_map## The following object is masked from 'package:ggpubr':
##
## get_legendscatterdens <- function(x) {
sp <- ggplot(x,aes_string(colnames(x)[1], colnames(x)[2])) + theme_minimal() + geom_point(alpha=0.3) + geom_density_2d()
xdens <- ggplot(x, aes_string(colnames(x)[1],fill=1)) + theme_minimal() + geom_density(alpha=.5)+ theme(axis.text.x = element_blank(), legend.position = "none" ) + labs(x=NULL)
ydens <- ggplot(x, aes_string(colnames(x)[2],fill=1)) + theme_minimal() + geom_density(alpha=.5) + theme(axis.text.y = element_blank(), axis.text.x = element_text(angle=90, vjust=0), legend.position = "none" ) + labs(x=NULL) + coord_flip()
g <- plot_grid(xdens,NULL,sp,ydens, ncol=2,nrow=2, rel_widths=c(4,1.4),rel_heights=c(1.4,4))
return(g)
}mz_bmi <- log(subset(d6, ZygosityGT == "MZ" )[,c("BMI1","BMI2")])
p_bmi_mz<-scatterdens(mz_bmi)
mz_aom<-subset(d6, ZygosityGT == "MZ" )[,c("Menstrual_AgeBegan1","Menstrual_AgeBegan2")]
p_aom_mz<-scatterdens(mz_aom)
mz_V178<-subset(d6, ZygosityGT == "MZ" )[,c("V1781","V1782")]
p_V178_mz<-scatterdens(mz_V178)
mz_V36<-subset(d6, ZygosityGT == "MZ" )[,c("V361","V362")]
p_V36_mz<-scatterdens(mz_V36)
dz_bmi <- log(subset(d6, ZygosityGT == "DZ" )[,c( "BMI1" , "BMI2" )])
p_bmi_dz<-scatterdens(dz_bmi)
dz_aom<-subset(d6, ZygosityGT == "DZ" )[,c("Menstrual_AgeBegan1","Menstrual_AgeBegan2")]
p_aom_dz<-scatterdens(dz_aom)
dz_V178<-subset(d6, ZygosityGT == "DZ" )[,c("V1781","V1782")]
p_V178_dz<-scatterdens(dz_V178)
dz_V36<-subset(d6, ZygosityGT == "DZ" )[,c("V361","V362")]
p_V36_dz<-scatterdens(dz_V36)
pBMI<-ggarrange(p_bmi_mz,p_bmi_dz + rremove("x.text"),
labels = c("A", "B"),
ncol = 2, nrow = 1)
pAoM<-ggarrange(p_aom_mz,p_aom_dz + rremove("x.text"),
labels = c("A", "B"),
ncol = 2, nrow = 1)
pV178<-ggarrange(p_V178_mz,p_V178_dz + rremove("x.text"),
labels = c("A", "B"),
ncol = 2, nrow = 1)
pV36<-ggarrange(p_V36_mz,p_V36_dz + rremove("x.text"),
labels = c("A", "B"),
ncol = 2, nrow = 1)cor.test(mz_bmi[,1],mz_bmi[,2], method= "spearman" ) ## Warning in cor.test.default(x, y, ...): Cannot compute exact p-value with
## ties##
## Spearman's rank correlation rho
##
## data: x and y
## S = 17655, p-value = 2.06e-07
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5958124cor.test(dz_bmi[,1],dz_bmi[,2], method= "spearman" ) ## Warning in cor.test.default(x, y, ...): Cannot compute exact p-value with
## ties##
## Spearman's rank correlation rho
##
## data: x and y
## S = 3832.8, p-value = 0.2189
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2272636cor.test(mz_aom[,1],mz_aom[,2], method= "spearman" ) ## Warning in cor.test.default(x, y, ...): Cannot compute exact p-value with
## ties##
## Spearman's rank correlation rho
##
## data: x and y
## S = 26715, p-value = 0.001517
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.38839cor.test(dz_aom[,1],dz_aom[,2], method= "spearman" ) ## Warning in cor.test.default(x, y, ...): Cannot compute exact p-value with
## ties##
## Spearman's rank correlation rho
##
## data: x and y
## S = 2547.6, p-value = 0.005531
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4863782About the results
Heritability is formally defined as the proportion of phenotypic variation (VP) that is due to variation in genetic values (VG).
### Broad-sense heritability
Defined as H2 = VG/VP, captures the proportion of phenotypic variation due to genetic values that may include effects due to dominance and epistasis. * dominance - possible allelic interactions within loci
* epistasis- between loci
Narrow-sense heritability
h2 = VA/VP, refers to the proportion of phenotypic variation that is due to additive genetic values (VA)
The phenotypic variance (VP) in a population is influenced by genetic variance (VG) and environmental sources (VE)
VP = VG + VE so then narrow sense is also defined as:
h2 = EA/(VG+VE)
The total amount of genetic variance can be divided into several groups, including additive variance (VA), dominance variance (VD), and epistatic variance (VI). VG = VA + VD + VI so then narrow sense is also:
h2 = VA/(VA+VD+VI+VE)
variance components
- additive genetic (A)
- common shared family environment (C)
- non-shared environmental (E)
- genetic dominance effects (D)
dd<-na.omit(d5)
dd$ZygosityGT <- factor(dd$ZygosityGT)
dd$Race<- factor(dd$Race)
l0 <- twinlm(BMI ~ 1+Age_in_Yrs+Race+Menstrual_AgeBegan, data=dd, DZ= "DZ" , zyg= "ZygosityGT" , id="Family_ID", type="aced", missing = T)
summary(l0)## Estimate Std. Error Z value
## BMI 29.98942 5.43080 5.5221
## sd(A) 1.76291 221.43510 0.0080
## sd(C) 0.46039 283.02369 0.0016
## sd(D) 3.63867 71.48488 0.0509
## sd(E) 2.96324 0.28443 10.4182
## BMI~Age_in_Yrs 0.16909 0.14142 1.1957
## BMI~RaceAsian/Nat. Hawaiian/Othr Pacific Is. -4.20867 2.27972 -1.8461
## BMI~RaceBlack or African Am. 4.83557 1.29975 3.7204
## BMI~RaceMore than one 0.54532 5.05986 0.1078
## BMI~RaceUnknown or Not Reported -0.70485 2.57185 -0.2741
## BMI~Menstrual_AgeBegan -0.74964 0.22699 -3.3025
## Pr(>|z|)
## BMI 3.35e-08
## sd(A) 0.9936479
## sd(C) 0.9987021
## sd(D) 0.9594042
## sd(E) < 2.2e-16
## BMI~Age_in_Yrs 0.2318241
## BMI~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.0648726
## BMI~RaceBlack or African Am. 0.0001989
## BMI~RaceMore than one 0.9141759
## BMI~RaceUnknown or Not Reported 0.7840359
## BMI~Menstrual_AgeBegan 0.0009581
##
## MZ-pairs/singletons DZ-pairs/singletons
## 64/16 31/21
##
## Variance decomposition:
## Estimate 2.5% 97.5%
## A 0.12264 -60.26376 60.50905
## C 0.00836 -20.14805 20.16478
## D 0.52248 -39.71361 40.75857
## E 0.34651 0.19970 0.49332
##
##
## Estimate 2.5% 97.5%
## Broad-sense heritability 0.64512 -19.51273 20.80298
##
## Estimate 2.5% 97.5%
## Correlation within MZ: 0.65349 0.48165 0.77694
## Correlation within DZ: 0.20031 -0.12655 0.48792
##
## 'log Lik.' -670.5197 (df=11)
## AIC: 1363.039
## BIC: 1394.75bmi_acde<-summary(l0)$acde
dim(bmi_acde)## [1] 4 3bmi_acde %>%
kable(digits = c(2,2,2)) %>%
kable_styling( position = "left") %>%
add_header_above(c(" "= 1, " " = 1, "Confidence Interval" = 2))| Estimate | 2.5% | 97.5% | |
|---|---|---|---|
| A | 0.12 | -60.26 | 60.51 |
| C | 0.01 | -20.15 | 20.16 |
| D | 0.52 | -39.71 | 40.76 |
| E | 0.35 | 0.20 | 0.49 |
l1 <- twinlm(Menstrual_AgeBegan ~ 1+Age_in_Yrs+Race, data=dd, DZ= "DZ" , zyg= "ZygosityGT" , id="Family_ID", type="aced", missing = T)
summary(l1) ## Estimate
## Menstrual_AgeBegan 14.458477
## sd(A) 0.621930
## sd(C) 0.523371
## sd(D) 0.647096
## sd(E) 1.040113
## Menstrual_AgeBegan~Age_in_Yrs -0.052359
## Menstrual_AgeBegan~RaceAsian/Nat. Hawaiian/Othr Pacific Is. -0.106312
## Menstrual_AgeBegan~RaceBlack or African Am. -1.223709
## Menstrual_AgeBegan~RaceMore than one -0.782993
## Menstrual_AgeBegan~RaceUnknown or Not Reported -1.047328
## Std. Error
## Menstrual_AgeBegan 1.238585
## sd(A) 10.546708
## sd(C) 4.198202
## sd(D) 6.765880
## sd(E) 0.093835
## Menstrual_AgeBegan~Age_in_Yrs 0.040572
## Menstrual_AgeBegan~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.657836
## Menstrual_AgeBegan~RaceBlack or African Am. 0.367372
## Menstrual_AgeBegan~RaceMore than one 1.476674
## Menstrual_AgeBegan~RaceUnknown or Not Reported 0.775562
## Z value
## Menstrual_AgeBegan 11.6734
## sd(A) 0.0590
## sd(C) 0.1247
## sd(D) 0.0956
## sd(E) 11.0845
## Menstrual_AgeBegan~Age_in_Yrs -1.2905
## Menstrual_AgeBegan~RaceAsian/Nat. Hawaiian/Othr Pacific Is. -0.1616
## Menstrual_AgeBegan~RaceBlack or African Am. -3.3310
## Menstrual_AgeBegan~RaceMore than one -0.5302
## Menstrual_AgeBegan~RaceUnknown or Not Reported -1.3504
## Pr(>|z|)
## Menstrual_AgeBegan < 2.2e-16
## sd(A) 0.9529767
## sd(C) 0.9007884
## sd(D) 0.9238056
## sd(E) < 2.2e-16
## Menstrual_AgeBegan~Age_in_Yrs 0.1968679
## Menstrual_AgeBegan~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.8716143
## Menstrual_AgeBegan~RaceBlack or African Am. 0.0008654
## Menstrual_AgeBegan~RaceMore than one 0.5959447
## Menstrual_AgeBegan~RaceUnknown or Not Reported 0.1768842
##
## MZ-pairs/singletons DZ-pairs/singletons
## 64/16 31/21
##
## Variance decomposition:
## Estimate 2.5% 97.5%
## A 0.17897 -11.71756 12.07550
## C 0.12674 -3.85818 4.11165
## D 0.19374 -7.74676 8.13425
## E 0.50055 0.31435 0.68675
##
##
## Estimate 2.5% 97.5%
## Broad-sense heritability 0.37271 -3.61992 4.36534
##
## Estimate 2.5% 97.5%
## Correlation within MZ: 0.49945 0.29176 0.66216
## Correlation within DZ: 0.26466 -0.03162 0.51818
##
## 'log Lik.' -399.2659 (df=10)
## AIC: 818.5318
## BIC: 847.3598aom_acde<-summary(l1)$acde
aom_acde %>%
kable(digits = c(2,2,2)) %>%
kable_styling( position = "left") %>%
add_header_above(c(" "= 1, " " = 1, "Confidence Interval" = 2))| Estimate | 2.5% | 97.5% | |
|---|---|---|---|
| A | 0.18 | -11.72 | 12.08 |
| C | 0.13 | -3.86 | 4.11 |
| D | 0.19 | -7.75 | 8.13 |
| E | 0.50 | 0.31 | 0.69 |
l2 <- twinlm(V178 ~ 1+BMI*Menstrual_AgeBegan+Age_in_Yrs+Race, data=d5,
DZ= "DZ" , zyg= "ZygosityGT" , id="Family_ID", type="aced", missing = T)
summary(l2) ## Estimate Std. Error
## V178 1.7149e+01 1.2005e+01
## sd(A) -1.4697e-12 5.2019e+00
## sd(C) 2.8295e-04 3.0033e+00
## sd(D) 3.3045e+00 3.1772e-01
## sd(E) 2.7786e+00 2.3867e-01
## V178~BMI -6.6793e-01 4.1285e-01
## V178~Menstrual_AgeBegan -9.9781e-01 8.7188e-01
## V178~Age_in_Yrs 3.6985e-03 1.2005e-01
## V178~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 1.2163e-01 1.9589e+00
## V178~RaceBlack or African Am. 2.3082e+00 1.1523e+00
## V178~RaceMore than one 9.8739e+00 4.3393e+00
## V178~RaceUnknown or Not Reported 7.9634e-01 2.2467e+00
## V178~BMI:Menstrual_AgeBegan 4.8170e-02 3.2900e-02
## Z value Pr(>|z|)
## V178 1.4285 0.15315
## sd(A) 0.0000 1.00000
## sd(C) 0.0001 0.99992
## sd(D) 10.4007 < 2e-16
## sd(E) 11.6417 < 2e-16
## V178~BMI -1.6178 0.10570
## V178~Menstrual_AgeBegan -1.1444 0.25244
## V178~Age_in_Yrs 0.0308 0.97542
## V178~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.0621 0.95049
## V178~RaceBlack or African Am. 2.0032 0.04516
## V178~RaceMore than one 2.2755 0.02288
## V178~RaceUnknown or Not Reported 0.3544 0.72300
## V178~BMI:Menstrual_AgeBegan 1.4641 0.14316
##
## MZ-pairs/singletons DZ-pairs/singletons
## 64/16 31/21
##
## Variance decomposition:
## Estimate 2.5% 97.5%
## A 0.00000 0.00000 0.00000
## C 0.00000 -0.00018 0.00018
## D 0.58581 0.43771 0.73391
## E 0.41419 0.26609 0.56229
##
##
## Estimate 2.5% 97.5%
## Broad-sense heritability 0.58581 0.43771 0.73391
##
## Estimate 2.5% 97.5%
## Correlation within MZ: 0.58581 0.41843 0.71471
## Correlation within DZ: 0.14645 0.10924 0.18326
##
## 'log Lik.' -640.335 (df=13)
## AIC: 1306.67
## BIC: 1344.146V178_acde<-summary(l2)$acde
V178_acde %>%
kable(digits = c(2,2,2)) %>%
kable_styling( position = "left") %>%
add_header_above(c(" "= 1, " " = 1, "Confidence Interval" = 2))| Estimate | 2.5% | 97.5% | |
|---|---|---|---|
| A | 0.00 | 0.00 | 0.00 |
| C | 0.00 | 0.00 | 0.00 |
| D | 0.59 | 0.44 | 0.73 |
| E | 0.41 | 0.27 | 0.56 |
l3 <- twinlm(V36 ~ 1+BMI*Menstrual_AgeBegan+Age_in_Yrs+Race, data=d5,
DZ= "DZ" , zyg= "ZygosityGT" , id="Family_ID", type="aced", missing = T)
y<-summary(l3)
names(y)## [1] "estimate" "zyg" "varEst" "KinshipGroup"
## [5] "varSigma" "heritability" "corMZ" "corDZ"
## [9] "acde" "logLik" "AIC" "BIC"
## [13] "type" "coef" "all" "vcov"y$estimate## Estimate Std. Error
## V36 23.40046089 9.18999022
## sd(A) 1.41698271 34.06610392
## sd(C) 0.73848423 21.84796310
## sd(D) 1.87918611 17.15289527
## sd(E) 2.16023438 0.19556098
## V36~BMI -0.81879925 0.31540241
## V36~Menstrual_AgeBegan -2.04426203 0.66679289
## V36~Age_in_Yrs 0.05423264 0.09192394
## V36~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 2.04076799 1.50218670
## V36~RaceBlack or African Am. -0.68257554 0.88046424
## V36~RaceMore than one -4.74735968 3.29601859
## V36~RaceUnknown or Not Reported 3.59766763 1.71487090
## V36~BMI:Menstrual_AgeBegan 0.06558587 0.02510620
## Z value Pr(>|z|)
## V36 2.54629878 1.088720e-02
## sd(A) 0.04159509 9.668215e-01
## sd(C) 0.03380106 9.730358e-01
## sd(D) 0.10955504 9.127623e-01
## sd(E) 11.04634659 2.283185e-28
## V36~BMI -2.59604629 9.430336e-03
## V36~Menstrual_AgeBegan -3.06581259 2.170793e-03
## V36~Age_in_Yrs 0.58997297 5.552088e-01
## V36~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 1.35853152 1.742951e-01
## V36~RaceBlack or African Am. -0.77524505 4.381949e-01
## V36~RaceMore than one -1.44033159 1.497736e-01
## V36~RaceUnknown or Not Reported 2.09792331 3.591192e-02
## V36~BMI:Menstrual_AgeBegan 2.61233736 8.992547e-03y## Estimate Std. Error Z value
## V36 23.400461 9.189990 2.5463
## sd(A) 1.416983 34.066104 0.0416
## sd(C) 0.738484 21.847963 0.0338
## sd(D) 1.879186 17.152895 0.1096
## sd(E) 2.160234 0.195561 11.0463
## V36~BMI -0.818799 0.315402 -2.5960
## V36~Menstrual_AgeBegan -2.044262 0.666793 -3.0658
## V36~Age_in_Yrs 0.054233 0.091924 0.5900
## V36~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 2.040768 1.502187 1.3585
## V36~RaceBlack or African Am. -0.682576 0.880464 -0.7752
## V36~RaceMore than one -4.747360 3.296019 -1.4403
## V36~RaceUnknown or Not Reported 3.597668 1.714871 2.0979
## V36~BMI:Menstrual_AgeBegan 0.065586 0.025106 2.6123
## Pr(>|z|)
## V36 0.010887
## sd(A) 0.966821
## sd(C) 0.973036
## sd(D) 0.912762
## sd(E) < 2.2e-16
## V36~BMI 0.009430
## V36~Menstrual_AgeBegan 0.002171
## V36~Age_in_Yrs 0.555209
## V36~RaceAsian/Nat. Hawaiian/Othr Pacific Is. 0.174295
## V36~RaceBlack or African Am. 0.438195
## V36~RaceMore than one 0.149774
## V36~RaceUnknown or Not Reported 0.035912
## V36~BMI:Menstrual_AgeBegan 0.008993
##
## MZ-pairs/singletons DZ-pairs/singletons
## 64/16 31/21
##
## Variance decomposition:
## Estimate 2.5% 97.5%
## A 0.18676 -17.41300 17.78652
## C 0.05073 -5.83187 5.93332
## D 0.32846 -11.42398 12.08090
## E 0.43406 0.26848 0.59963
##
##
## Estimate 2.5% 97.5%
## Broad-sense heritability 0.51522 -5.37077 6.40121
##
## Estimate 2.5% 97.5%
## Correlation within MZ: 0.56594 0.37818 0.70898
## Correlation within DZ: 0.22622 -0.17610 0.56378
##
## 'log Lik.' -578.4964 (df=13)
## AIC: 1182.993
## BIC: 1220.469V36_acde<-summary(l3)$acde
V36_acde %>%
kable(digits = c(2,2,2)) %>%
kable_styling( position = "left") %>%
add_header_above(c(" "= 1, " " = 1, "Confidence Interval" = 2))| Estimate | 2.5% | 97.5% | |
|---|---|---|---|
| A | 0.19 | -17.41 | 17.79 |
| C | 0.05 | -5.83 | 5.93 |
| D | 0.33 | -11.42 | 12.08 |
| E | 0.43 | 0.27 | 0.60 |
x0<-summary(l0)
x1<-summary(l1)
x2<-summary(l2)
x3<-summary(l3)
y0<-as.data.frame(x0[9])
y1<-as.data.frame(x1[9])
y2<-as.data.frame(x2[9])
y3<-as.data.frame(x3[9])
y0$fac<-row.names(y0)
y1$fac<-row.names(y1)
y2$fac<-row.names(y2)
y3$fac<-row.names(y3)pd <- position_dodge(0.1)
n0= 3
plt0<-ggplot(y0, aes(x=fac, y=acde.Estimate, color=fac)) +
ggtitle("BMI") +
geom_point(position=position_dodge(), stat="identity",size=n0) +
geom_errorbar(aes(ymin=acde.2.5., ymax=acde.97.5.),
size=1,
width=.5,
position=position_dodge(.9))+
geom_hline(aes(yintercept = 0), color = "grey")+
scale_color_manual(values = c("#000000", "#999999", "#CCCCCC", "#666666"))+
theme_classic()+
scale_x_discrete(name="Genetic component")+
scale_y_continuous(name="Estimate")+
theme(axis.title.x = element_text(size=20), axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text(size=20), axis.text.y = element_text(size=20))+
theme(legend.text = element_text(size=20))+
theme(legend.title = element_text(size=20, face="bold"))+
theme(legend.position="none")
plt1<-ggplot(y1, aes(x=fac, y=acde.Estimate, color=fac)) +
ggtitle("Age at onset of menses") +
geom_point(position=position_dodge(), stat="identity",size=n0) +
geom_errorbar(aes(ymin=acde.2.5., ymax=acde.97.5.),
size=1,
width=.5,
position=position_dodge(.9))+
geom_hline(aes(yintercept = 0), color = "grey")+
scale_color_manual(values = c("#000000", "#999999", "#CCCCCC", "#666666"))+
theme_classic()+
scale_x_discrete(name="Genetic component")+
scale_y_continuous(name="Estimate")+
theme(axis.title.x = element_text(size=20), axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text(size=20), axis.text.y = element_text(size=20))+
theme(legend.text = element_text(size=20))+
theme(legend.title = element_text(size=20, face="bold"))+
theme(legend.position="none")
plt2<-ggplot(y2, aes(x=fac, y=acde.Estimate, color=fac)) +
ggtitle("Correlation between IC12 and IC13") +
theme(plot.title = element_text(size = 20, face = "bold"))+
geom_point(position=position_dodge(), stat="identity",size=n0) +
geom_errorbar(aes(ymin=acde.2.5., ymax=acde.97.5.),
size=1,
width=.5,
position=position_dodge(.9))+
geom_hline(aes(yintercept = 0), color = "grey")+
scale_color_manual(values = c("#000000", "#999999", "#CCCCCC", "#666666"))+
theme_classic()+
scale_x_discrete(name="Genetic component")+
scale_y_continuous(name="Estimate")+
theme(axis.title.x = element_text(size=20), axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text(size=20), axis.text.y = element_text(size=20))+
theme(legend.text = element_text(size=20))+
theme(legend.title = element_text(size=20, face="bold"))+
theme(legend.position="none")
plt3<-ggplot(y3, aes(x=fac, y=acde.Estimate, color=fac)) +
ggtitle("Correlation between IC3 and IC6") +
theme(plot.title = element_text(size = 20, face = "bold"))+
geom_point(position=position_dodge(), stat="identity",size=n0) +
geom_errorbar(aes(ymin=acde.2.5., ymax=acde.97.5.),
size=1,
width=.5,
position=position_dodge(.9))+
geom_hline(aes(yintercept = 0), color = "grey")+
scale_color_manual(values = c("#000000", "#999999", "#CCCCCC", "#666666"))+
theme_classic()+
scale_x_discrete(name="Genetic component")+
scale_y_continuous(name="Estimate")+
theme(axis.title.x = element_text(size=20), axis.text.x = element_text(size=20))+
theme(axis.title.y = element_text(size=20), axis.text.y = element_text(size=20))+
theme(legend.text = element_text(size=20))+
theme(legend.title = element_text(size=20, face="bold"))+
theme(legend.position="none")
acde<-ggarrange(plt0,plt1,plt2,plt3 + rremove("x.text"),
labels = c("A", "B","C","D"),
ncol = 2, nrow = 2)## Warning: Width not defined. Set with `position_dodge(width = ?)`
## Warning: Width not defined. Set with `position_dodge(width = ?)`
## Warning: Width not defined. Set with `position_dodge(width = ?)`
## Warning: Width not defined. Set with `position_dodge(width = ?)`acde
## Summary
It appears that the genetic contribution in this sample to BMI and Age of onset of menses is minimal and predomiently through the enviroment. However, we see a large effect of both genetic domience and enviroment on connectivity between 12 and 13. Much more modest differences in the 3 and 6 connectivity with the enviroment.